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DOE OSTI2021

Diverse Super-Resolution (diversity_SR) [SWR-21-60]

Deep learning tools for enhancing the spatial resolution of wind data. The software is developed in Python using the TensorFlow deep learning package. Models for diversity super-resolution is provided. Included in the package are pretrained models with example code/data to perform the super-resolution as well as tools of training models for different enhancement- or data-types. The super-resolution is an inherently ill-conditioned problem, with multiple high-resolution fields plausibly mapping to the same coarse field. Considitional GANs provide a framework for generating a distribution of high-resolution realizations from a given low-resolution input. Stochastic estimation is used to inform the network of the expected degree and location of sub-grid diversity. The package includes a pretrained network to generate distributions of 10x-enhanced fields of wind data.

Glaws, Andrew↗

DOE OSTI2013

Cementitious Barriers Partnership (CBP): Using the CBP Software Toolbox to Simulate Sulfate Attack and Carbonation of Concrete Structures - 13481

The Cementitious Barriers Partnership (CBP) Project is a multi-disciplinary, multi-institutional collaboration supported by the U.S. Department of Energy Office of Tank Waste Management. The CBP project has developed a set of integrated modeling tools and leaching test methods to help improve understanding and prediction of the long-term hydraulic and chemical performance of cementitious materials used in nuclear applications. State-of-the-art modeling tools, including LeachXS{sup TM}/ORCHESTRA and STADIUM{sup R}, were selected for their demonstrated abilities to simulate reactive transport and degradation in cementitious materials. The new U.S. Environmental Protection Agency leaching test methods based on the Leaching Environmental Assessment Framework (LEAF), now adopted as part of the SW-846 RCRA methods, have been used to help make the link between modeling and experiment. Although each of the CBP tools has demonstrated utility as a standalone product, coupling the models over relevant spatial and temporal solution domains can provide more accurate predictions of cementitious materials behavior over relevant periods of performance. The LeachXS{sup TM}/ORCHESTRA and STADIUM{sup R} models were first linked to the GoldSim Monte Carlo simulator to better and more easily characterize model uncertainties and as a means to coupling the models allowing linking to broader performance assessment evaluations that use CBP results for a source term. Two important degradation scenarios were selected for initial demonstration: sulfate ingress / attack and carbonation of cementitious materials. When sufficient sulfate is present in the pore solution external to a concrete barrier, sulfate can diffuse into the concrete, react with the concrete solid phases, and cause cracking that significantly changes the transport and structural properties of the concrete. The penetration of gaseous carbon dioxide within partially saturated concrete usually initiates a series of carbonation reactions with both dissolved ions and the hydrated cement paste. The carbonation process itself does not have a negative effect, per se, on the paste physical properties and can even result in reduced porosity and can help form a protective layer at the surface of concrete. However, carbonation has been shown to increase leaching of some constituents and can potentially have a detrimental effect on reinforced concrete structures by lowering pH to ca. 9 and de-passivating embedded steel (e.g. rebar) and accelerating corrosion, which are important processes related to high-level waste tank integrity and closure evaluations. The use of the CBP Software Toolbox to simulate these important degradation phenomena for both concrete vaults and high-level waste tanks are demonstrated in this paper. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Non-Adiabatic Excited State Molecular Dynamics Methodologies: comparison and convergence

Direct atomistic simulation of nonadiabatic molecular dynamics is a challenging goal that allows important insights into fundamental physical phenomena. A variety of frameworks, ranging from fully quantum treatment of nuclei to semiclassical and mixed quantum–classical approaches, were developed. These algorithms are then coupled to specific electronic structure techniques. Such diversity and lack of standardized implementation make it difficult to compare the performance of different methodologies when treating realistic systems. Here, we compare three popular methods for large chromophores: Ehrenfest, surface hopping, and multiconfigurational Ehrenfest with ab initio multiple cloning (MCE-AIMC). These approaches are implemented in the NEXMD software, which features a common computational chemistry model. The resulting comparisons reveal the method performance for population relaxation and coherent vibronic dynamics. Finally, we study the numerical convergence of MCE-AIMC algorithms by considering the number of trajectories, cloning thresholds, and Gaussian wavepacket width. Our results provide helpful reference data for selecting an optimal methodology for simulating excited-state molecular dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Quantitative imaging and automated fuel pin identification for passive gamma emission tomography

Compliance of member States to the Treaty on the Non-Proliferation of Nuclear Weapons is monitored through nuclear safeguards. The Passive Gamma Emission Tomography (PGET) system is a novel instrument developed within the framework of the International Atomic Energy Agency (IAEA) project JNT 1510, which included the European Commission, Finland, Hungary and Sweden. The PGET is used for the verification of spent nuclear fuel stored in water pools. Advanced image reconstruction techniques are crucial for obtaining high-quality cross-sectional images of the spent-fuel bundle to allow inspectors of the IAEA to monitor nuclear material and promptly identify its diversion. In this work, we have developed a software suite to accurately reconstruct the spent-fuel cross sectional image, automatically identify present fuel rods, and estimate their activity. Unique image reconstruction challenges are posed by the measurement of spent fuel, due to its high activity and the self-attenuation. While the former is mitigated by detector physical collimation, we implemented a linear forward model to model the detector responses to the fuel rods inside the PGET, to account for the latter. The image reconstruction is performed by solving a regularized linear inverse problem using the fast-iterative shrinkage-thresholding algorithm. We have also implemented the traditional filtered back projection (FBP) method based on the inverse Radon transform for comparison and applied both methods to reconstruct images of simulated mockup fuel assemblies. Higher image resolution and fewer reconstruction artifacts were obtained with the inverse-problem approach, with the mean-square-error reduced by 50%, and the structural-similarity improved by 200%. We then used a convolutional neural network (CNN) to automatically identify the bundle type and extract the pin locations from the images; the estimated activity levels finally being compared with the ground truth. The proposed computational methods accurately estimated the activity levels of the present pins, with an associated uncertainty of approximately 5%.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2018

The ATLAS Trigger Simulation with Legacy Software

Physics analyses at the LHC require accurate simulations of the detector response and the event selection processes, generally done with the most recent software releases. The trigger response simulation is crucial for determination of overall selection efficiencies and signal sensitivities and should be done with the same software release with which data were recorded. This requires potentially running with software dating many years back, the so-called legacy software, in which algorithms and configuration may differ from their current implementation. Therefore having a strategy for running legacy software in a modern environment becomes essential when data simulated for past years start to present a sizeable fraction of the total. The requirements and possibilities for such a simulation scheme within the ATLAS software framework were examined and a proof-of-concept simulation chain has been successfully implemented. One of the greatest challenges was the choice of a data format which promises long term compatibility with old and new software releases. Over the time periods envisaged, data format incompatibilities are also likely to emerge in databases and other external support services. Software availability may become an issue, when e.g. the support for the underlying operating system might stop. The encountered problems and developed solutions will be presented, and proposals for future development will be discussed. Some ideas reach beyond the retrospective trigger simulation scheme in ATLAS as they also touch more generally aspects of data preservation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

MetaboDirect: an analytical pipeline for the processing of FT-ICR MS-based metabolomic data

Background: Microbiomes are now recognized as the main drivers of ecosystem function ranging from the oceans and soils to humans and bioreactors. However, a grand challenge in microbiome science is to characterize and quantify the chemical currencies of organic matter (i.e., metabolites) that microbes respond to and alter. Critical to this has been the development of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), which has drastically increased molecular characterization of complex organic matter samples, but challenges users with hundreds of millions of data points where readily available, user-friendly, and customizable software tools are lacking. Results: Here, we build on years of analytical experience with diverse sample types to develop MetaboDirect, an open-source, command-line-based pipeline for the analysis (e.g., chemodiversity analysis, multivariate statistics), visualization (e.g., Van Krevelen diagrams, elemental and molecular class composition plots), and presentation of direct injection high-resolution FT-ICR MS data sets after molecular formula assignment has been performed. When compared to other available FT-ICR MS software, MetaboDirect is superior in that it requires a single line of code to launch a fully automated framework for the generation and visualization of a wide range of plots, with minimal coding experience required. Among the tools evaluated, MetaboDirect is also uniquely able to automatically generate biochemical transformation networks (ab initio) based on mass differences (mass difference network-based approach) that provide an experimental assessment of metabolite connections within a given sample or a complex metabolic system, thereby providing important information about the nature of the samples and the set of microbial reactions or pathways that gave rise to them. Finally, for more experienced users, MetaboDirect allows users to customize plots, outputs, and analyses. Conclusion: Application of MetaboDirect to FT-ICR MS-based metabolomic data sets from a marine phage-bacterial infection experiment and a Sphagnum leachate microbiome incubation experiment showcase the exploration capabilities of the pipeline that will enable the research community to evaluate and interpret their data in greater depth and in less time. It will further advance our knowledge of how microbial communities influence and are influenced by the chemical makeup of the surrounding system. The source code and User’s guide of MetaboDirect are freely available through (https://github.com/Coayala/MetaboDirect) and (https://metabodirect.readthedocs.io/en/latest/), respectively.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

wa-hls4ml: A GNN Surrogate Model for hls4ml

Recent advancements in use of machine learning techniques on field-programmable gate arrays (FPGAs) have allowed for implementation of embedded neural networks with extremely low latency. This is invaluable for particle detectors at the Large Hadron Collider, where latency and used area must be strictly bounded. The hls4ml framework is a procedure for converting from trained machine learning model software, to a synthesis result that can be used on an FPGA. However, running the pipeline is a time-consuming procedure, and there is a strong risk of failure. In particular, it is possible that the model is unable to be converted into a synthesis result, or that the resource consumption of the model will exceed the resources of the target FPGA. To aid with this development, we introduce wa-hls4ml, a surrogate model which uses a graph neural network to emulate the structure of the source models. The goal is to estimate the chance of success and resource consumption of an arbitrary model when passed through the hls4ml procedure, without the time consumption of actually running the pipeline.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

NEML2: A High Performance Library for Constitutive Modeling

NEML2, the New Engineering Material model Library, version 2, is an offshoot of NEML, an earlier material modeling code developed at Argonne National Laboratory. NEML2 extends the key philosophy of its predecessor, i.e., material models are flexible, modular, and can be built from smaller blocks. It also provides modern features that do not exist in the framework of its predecessor such as material model vectorization, automatic differentiation, device-portable just-in-time compilation, operator fusion, lazy tensor evaluation, etc. Moreover, NEML2 can seamlessly integrate with the popular machine learning package PyTorch to take advantage of modern and fast-growing machine learning techniques. In this fiscal year, the development of core library features and capabilities are complete. The purpose of this report is not to serve as a verbatim copy of the software API reference (which is available online at https://reverendbedford.github.io/neml2/). Instead, this report documents the motivation, implementation, design choices, and usage of each core capability as well as their applications in solving practical engineering problems. This report is compiled based on the NEML2 major release 2.0.0.

36 MATERIALS SCIENCE↗

DOE OSTI2026

APOLLO: a facility-scale differentiable virtual accelerator for Fermilab

As the design complexity of modern accelerators grows, there is more interest in using advanced simulations that have fast execution time or yield additional insights like gradients. The FAST/IOTA facility has been working on implementing and experimentally validating an end-to-end digital twin that is both fast and gradient-aware, allowing for rapid prototyping of new software and experiments with minimal beam time costs. Our framework integrates physics and ML codes for linac and ring simulation through a set of generic interfaces between surrogate and physics-based sections. To reproduce device inputs and outputs, system state is exposed as a deterministic discrete event simulator. Because Fermilab is undergoing control system transition, both EPICS and ACNET frontends are supported. Recently, we have begun transitioning to a new community lattice standard, PALS, as well as developing standardized infrastructure for data ingest and normalization to prepare for model calibration during FAST proton injector commissioning. We discuss implementation details as well as challenges, and future plans to extend modelling to main complex proton accelerators like PIPII and Booster.

Kuklev, Nikita [Fermilab]↗

DOE OSTI2024

The ATLAS Workflow Management System Evolution in the LHC Run3 and towards the High-Luminosity LHC era

The ATLAS experiment has 18+ years of experience using workload management systems to deploy and develop workflows to process and to simulate data on the distributed computing infrastructure. Simulation, processing and analysis of LHC experiment data require the coordinated work of heterogeneous computing resources. In particular, the ATLAS experiment utilizes the resources of 250 computing centers worldwide, the power of supercomputing centres, and national, academic and commercial cloud computing resources. In this contribution, we present new techniques for cost-effectively improving efficiency introduced in workflow management system software. The evolution from a mesh framework to new types of computing facilities such as cloud and HPCs is described, as well as new types of production and analysis workflows.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

CostMAP: an open-source software package for developing cost surfaces using a multi-scale search kernel

Cost surfaces are a crucial aspect of route optimization and least cost path (LCP) calculations and are used in awide range of disciplines including computer science, landscape ecology, and energy-infrastructure modeling. Linear features introduce akey weakness to traditional routing calculations along cost surfaces because they cannot identify whether moving from acell to its adjacent neighbors constitutes crossing alinear barrier (increased cost) or following acorridor (reduced cost). Following and avoiding linear features can drastically change predicted routes. We introduce an approach to address this adjacency issue using asearch kernel that determines these critical barriers and corridors. We have built this method into anew Java-based open-source software package– CostMAP (cost surface multi-layer aggregation program)– which calculates cost surfaces and cost networks using the search kernel. CostMAP allows users to input multiple GIS data layers and to set weights and rules for developing aweighted-cost network. We compare CostMAP performance with traditional cost surface approaches and show significant performance gains– both following corridors and avoiding barriers– by modeling the movement of alarge terrestrial animal– the Baird’s Tapir (Tapirus bairdii)– in amovement ecology framework and by modeling pipeline routing for carbon capture and storage (CCS).

58 GEOSCIENCES↗

DOE OSTI2014

New Python-based methods for data processing

Abstract Not Provided

XFEL↗

DOE OSTI2015

Albany v. 3.0

The Albany code is a general-purpose finite element code for solving partial differential equations (PDEs). Albany is a research code that demonstrates how a PDE code can be built by interfacing many of the open-source software libraries that are released under Sandia's Trilinos project. Part of the mission of Albany is to be a testbed for new Trilinos libraries, to refine their methods, usability, and interfaces. Albany includes hooks to optimization and uncertainty quantification algorithms, including those in Trilinos as well as those in the Dakota toolkit. Because of this, Albany is a desirable starting point for new code development efforts that wish to make heavy use of Trilinos. Albany is both a framework and the host for specific finite element applications. These applications have project names, and can be controlled by configuration option when the code is compiled, but are all developed and released as part of the single Albany code base, These include LCM, QCAD, FELIX, Aeras, and ATO applications.

SciDAC↗

DOE OSTI2017

Material Protection, Accounting and Control Technologies (MPACT) Implementation Plan: Lab-Scale Demonstration of Advanced Safeguard and Security Systems

The Materials Protection, Accounting, and Control Technologies (MPACT) campaign is working towards the goal of developing and demonstrating the next generation of nuclear materials management for the peaceful use of civilian nuclear energy. A major milestone of the campaign is the “Lab-scale demonstration of an advanced safeguards and security system,” due in 2020. This implementation plan describes how tools developed in the MPACT campaign will be used to meet this milestone. These tools consist of instrumentation and devices as well as computer software for modelling, simulation and integration of data and information for safeguard and security analysis. The plan is focused on demonstrating an advanced safeguard and security systems for electrochemical processing plants, but also identifies activities needed to address advanced reactor, aqueous processing and other advanced fuel cycle facilities. A key component to achieving the 2020 milestone is the development of a virtual distributed test bed that incorporates the instruments, data, analysis tools, and models currently under development and provides a framework to link and normalize the data in a way that simulates a facility, including uncertainty quantification. This virtual distributed test bed will integrate computer simulated facilities that make up a fuel cycle of interest, with distributed test beds located in actual operating facilities and laboratories. The MPACT campaign recently issued roadmaps for both Modelling & Simulation and Advanced Integration which address this component, and activities necessary to achieve the 2020 milestone. Research, development and testing of specific technologies have been supported by the MPACT campaign in coordination with other DOE-NE and DOE-NNSA programs. These technologies provide the essential data needed by the safeguard and security systems, and feed into the data analysis and facility models. This plan will identify these technologies, their current status and activities needed to demonstrate the technologies at the laboratory scale. Wherever possible, test of technologies in field conditions using suitable nuclear materials will be used to define performance. Where necessary, surrogate materials and/or simulation will be used to provide useful information for the demonstration. When completed, the 2020 milestone will provide a demonstration of advanced safeguard and security technologies, including advanced modelling, simulation and analysis approaches, applied to electrochemical processing. It will also identify applications of the technology and approaches in other advanced fuel cycle and reactor concepts.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2015

Characterization of extreme precipitation within atmospheric river events over California

Atmospheric rivers (ARs) are large, spatially coherent weather systems with high concentrations of elevated water vapor. These systems often cause severe downpours and flooding over the western coastal United States – and with the availability of more atmospheric moisture in the future under global warming we expect ARs to play an important role as potential causes of extreme precipitation changes. Therefore, we aim to investigate changes in extreme precipitation properties correlated with AR events in a warmer climate, which are large-scale meteorological patterns affecting the weather and climate of California. We have recently developed the TECA (Toolkit for Extreme Climate Analysis) software for automatically identifying and tracking features in climate data sets. Specifically, we can now identify ARs that make landfall on the western coast of North America. Based on this detection procedure, we can investigate the impact of ARs by exploring the spatial extent of AR precipitation using climate model (CMIP5) simulations and characterize spatial patterns of dependence for future projections between AR precipitation extremes under climate change within the statistical framework. Our results show that AR events in the future RCP (Representative Concentration Pathway)8.5 scenario (2076–2100) tend to produce heavier rainfall with higher frequency and longer days than events from the historical run (1981–2005). We also find that the dependence between extreme precipitation events has a shorter spatial range, within localized areas in California, under the high future emissions scenario than under the historical run.

Jeon, S.↗

DOE OSTI2016

Albany v. 3.0

Salinger, Andrew↗

DOE OSTI2017

Enhancing the physical modeling capability of open-source MFIX-DEM software for handling particle size polydispersity: Implementation and validation

Multiphase flows are ubiquitous in many industrial processes. The inherent coupling of different phases poses many unique challenges in predicting and effectively controlling these processes. Hence, computational modeling and simulation offers a viable approach to overcome these challenges. Here in this study, we present recent development efforts for enhancing the physical modeling capabilities of an open-source computational modeling tool for real life industrial multiphase processes by enabling particle-size polydispersity and demonstrating with an associated validation study. The proposed implementation was performed in MFIX open-source framework due to its unique feature of tightly integrated computational fluid dynamics and discrete element method solvers for simulating coupled continuum fluid and granular flows. We have implemented the polydispersity feature in a minimally invasive way and provided means to allow easy specification of an arbitrary particle size distribution function, which also enables the user to easily handle an arbitrary number of solid phases, each possessing a distinct arbitrary particle-size distribution. To establish the credibility of improvements, we have carried out a preliminary verification and validation (V&V) study for the polydispersity feature by employing a hopper bin discharge problem, which is frequently encountered in industrial applications. Specifically, two types of micro-glass beads with distinct size distributions are used to fill the hopper in two possible packing arrangements, i.e., well-mixed and layered configurations, with varying mass (particle number) ratios. The experimentally obtained discharge dynamics (e.g., normalized discharge mass fraction for one of the phases versus the overall discharge mass fraction) for different systems is found to be in excellent agreement with the corresponding simulation results.

42 ENGINEERING↗

DOE OSTI2013

Sandia Higher Order Elements (SHOE) v 0.5 alpha

SHOE is research code for characterizing and visualizing higher-order finite elements; it contains a framework for defining classes of interpolation techniques and element shapes; methods for interpolating triangular, quadrilateral, tetrahedral, and hexahedral cells using Lagrange and Legendre polynomial bases of arbitrary order; methods to decompose each element into domains of constant gradient flow (using a polynomial solver to identify critical points); and an isocontouring technique that uses this decomposition to guarantee topological correctness. Please note that this is an alpha release of research software and that some time has passed since it was actively developed; build- and run-time issues likely exist.

Thompson, David↗